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      A Real-Time Monitoring System of Industry Carbon Monoxide Based on Wireless Sensor Networks

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          Abstract

          Carbon monoxide (CO) burns or explodes at over-standard concentration. Hence, in this paper, a Wifi-based, real-time monitoring of a CO system is proposed for application in the construction industry, in which a sensor measuring node is designed by low-frequency modulation method to acquire CO concentration reliably, and a digital filtering method is adopted for noise filtering. According to the triangulation, the Wifi network is constructed to transmit information and determine the position of nodes. The measured data are displayed on a computer or smart phone by a graphical interface. The experiment shows that the monitoring system obtains excellent accuracy and stability in long-term continuous monitoring.

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          Energy efficiency in wireless sensor networks: A top-down survey

          Tifenn Rault, Abdelmadjid Bouabdallah, Yacine Challal (2014)
          Article 0 comments Cited 105 times – based on 0 reviews     Review now
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            Gaussian Processes for Data-Efficient Learning in Robotics and Control

            Marc Peter Deisenroth, Dieter Fox, Carl Rasmussen (2015)
            Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.
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              Smart Sensors Network for Air Quality Monitoring Applications

              O.A. Postolache, J.M.D. Pereira, P.M.B.S. Girao (2009)
              Article 0 comments Cited 29 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Leonhard M. Reindl: Role: Academic Editor
                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 20 November 2015
                Publication date Collection: November 2015
                Volume: 15
                Issue: 11
                Pages: 29535-29546
                Affiliations
                [1 ]School of Electronic Information Engineering, Tianjin University, 92 Weijin Road, Tianjin 300072, China; E-Mails: yangjiachen@ 123456tju.edu.cn (J.Y.); zhoujx@ 123456tju.edu.cn (J.Z.)
                [2 ]Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen 518055, China; E-Mail: zh.lv@ 123456siat.ac.cn
                [3 ]School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China; E-Mail: weiwei@ 123456xaut.edu.cn
                [4 ]Department of Electrical and Computer Engineering, West Virginia University, Montgomery, WV 25136, USA
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: Houbing.Song@ 123456mail.wvu.edu ; Tel.: +1-304-442-3076; Fax: +1-304-442-3330.
                Article
                Publisher ID: sensors-15-29535
                DOI: 10.3390/s151129535
                PMC ID: 4701347
                PubMed ID: 26610511
                SO-VID: ce04c8e2-855e-43d6-bad8-ca20e0ae4ba8
                Copyright © © 2015 by the authors; licensee MDPI, Basel, Switzerland.
                License:

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 30 July 2015
                Date accepted : 17 November 2015
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: co detection,real-time monitor,sensor networks,wifi
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: co detection, real-time monitor, sensor networks, wifi

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